This paper presents a novel, wearable navigation system for visually impaired and blind pedestrians that combines a global positioning system (GPS) for user outdoor localization and tactile-foot stimulation for information presentation. Real-time GPS data provided by a smartphone are processed by dedicated navigation software to determine the directions to a destination. Navigational directions are then encoded as vibrations and conveyed to the user via a tactile display that inserts into the shoe. The experimental results showed that users were capable of recognizing with high accuracy the tactile feedback provided to their feet. The preliminary tests conducted in outdoor locations involved two blind users who were guided along 380–420 m predetermined pathways, while sharing the space with other pedestrians and facing typical urban obstacles. The subjects successfully reached the target destinations. The results suggest that the proposed system enhances independent, safe navigation of blind pedestrians and show the potential of tactile-foot stimulation in assistive devices.

This paper describes the state of the art in land mine detection technology and algorithms. Landmine detection is a growing concern due to the danger of buried landmines to people's lives, economic growth and development. Most of the injured people have no connection with the reason why the mines were placed. There are 50–100 million landmines in more than 80 countries around the world. Deactivation is estimated at 100 000 mines per year, against the nearly 2 million mines laid annually. In this paper we describe and analyse sensor technology available including state‐of‐the‐art technology such as ground penetrating radar (GPR), electromagnetic induction (EMI) and nuclear quadrupole resonance (NQR) among others. Robotics, data processing and algorithms are mentioned, considering support vectors, sensor fusion, neural networks, etc. Finally, we establish conclusions highlighting the need to improve not only the way images are acquired, but the way this information is processed and compared.

The detection of cracks is an important monitoring task in civil engineering infrastructure devoted to ensuring durability, structural safety, and integrity. It has been traditionally performed by visual inspection, and the measurement of crack width has been manually obtained with a crack-width comparator gauge (CWCG). Unfortunately, this technique is time-consuming, suffers from subjective judgement, and is error-prone due to the difficulty of ensuring a correct spatial measurement as the CWCG may not be correctly positioned in accordance with the crack orientation. Although algorithms for automatic crack detection have been developed, most of them have specifically focused on solving the segmentation problem through Deep Learning techniques failing to address the underlying problem: crack width evaluation, which is critical for the assessment of civil structures. This paper proposes a novel automated method for surface cracking width measurement based on digital image processing techniques. Our proposal consists of three stages: anisotropic smoothing, segmentation, and stabilized central points by k-means adjustment and allows the characterization of both crack width and curvature-related orientation. The method is validated by assessing the surface cracking of fiber-reinforced earthen construction materials. The preliminary results show that the proposal is robust, efficient, and highly accurate at estimating crack width in digital images. The method effectively discards false cracks and detects real ones as small as 0.15 mm width regardless of the lighting conditions.

Commercial polypropylene fibers are incorporated as reinforcement of cement-based materials to improve their mechanical and damage performances related to properties such as tensile and flexural strength, toughness, spalling and impact resistance, delay formation of cracks and reducing crack widths. Yet, the production of these polypropylene fibers generates economic costs and environmental impacts and, therefore, the use of alternative and more sustainable fibers has become more popular in the research materials community. This paper addresses the characterization of recycled polypropylene fibers (RPFs) obtained from discarded domestic plastic sweeps, whose morphological, physical and mechanical properties are provided in order to assess their implementation as fiber-reinforcement in cement-based mortars. An experimental program addressing the incorporation of RPFs on the mechanical-damage performance of mortars, including a sensitivity analysis on the volumes and lengths of fiber, is developed. Using analysis of variance, this paper shows that RPFs statistically enhance flexural toughness and impact strength for high dosages and long fiber lengths. On the contrary, the latter properties are not statistically modified by the incorporation of low dosages and short lengths of RPFs, but still in these cases the incorporation of RPFs in mortars have the positive environmental impact of waste encapsulation. In the case of average compressive and flexural strength of mortars, these properties are not statistically modified when adding RPFs.